Machine/deep learning for software engineering: A systematic literature review
Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …
Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning
Abstract Software Fault Prediction (SFP) is an important process to detect the faulty
components of the software to detect faulty classes or faulty modules early in the software …
components of the software to detect faulty classes or faulty modules early in the software …
Iterated feature selection algorithms with layered recurrent neural network for software fault prediction
Software fault prediction (SFP) is typically used to predict faults in software components.
Machine learning techniques (eg, classification) are widely used to tackle this problem. With …
Machine learning techniques (eg, classification) are widely used to tackle this problem. With …
Backpropagation Neural Network optimization and software defect estimation modelling using a hybrid Salp Swarm optimizer-based Simulated Annealing Algorithm
Abstract Software Defect Estimation (SDE) is a fundamental problem solving mechanism in
the field of software engineering (SE). SDE is a task that identifies software models that are …
the field of software engineering (SE). SDE is a task that identifies software models that are …
Enhanced binary moth flame optimization as a feature selection algorithm to predict software fault prediction
Software fault prediction (SFP) is a complex problem that meets developers in the software
development life cycle. Collecting data from real software projects, either while the …
development life cycle. Collecting data from real software projects, either while the …
[HTML][HTML] Salp swarm optimizer for modeling the software fault prediction problem
S Kassaymeh, S Abdullah, MA Al-Betar… - Journal of King Saud …, 2022 - Elsevier
This paper proposes the salp swarm algorithm (SSA) combined with a backpropagation
neural network (BPNN) to solve the software fault prediction (SFP) problem. The SFP …
neural network (BPNN) to solve the software fault prediction (SFP) problem. The SFP …
How bad can a bug get? an empirical analysis of software failures in the openstack cloud computing platform
Cloud management systems provide abstractions and APIs for programmatically configuring
cloud infrastructures. Unfortunately, residual software bugs in these systems can potentially …
cloud infrastructures. Unfortunately, residual software bugs in these systems can potentially …
Iterative software fault prediction with a hybrid approach
E Erturk, EA Sezer - Applied Soft Computing, 2016 - Elsevier
In this study, we consider a software fault prediction task that can assist a developer during
the lifetime of a project. We aim to improve the performance of software fault prediction task …
the lifetime of a project. We aim to improve the performance of software fault prediction task …
Studying aging-related bug prediction using cross-project models
In long running systems, software tends to encounter performance degradation and
increasing failure rate during execution. This phenomenon has been named software aging …
increasing failure rate during execution. This phenomenon has been named software aging …
An empirical study of fault triggers in deep learning frameworks
Deep learning frameworks play a key rule to bridge the gap between deep learning theory
and practice. With the growing of safety-and security-critical applications built upon deep …
and practice. With the growing of safety-and security-critical applications built upon deep …